基于对抗图卷积的网络表征学习框架

doi:10.16451/j.cnki.issn1003-6059.201911009

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Abstract The existing network representation methods and their related variants are focused on preserving network topology structure or minimizing reconstruction error. However, data distribution of latent codes is ignored. To solve the problem, an adversarial graph convolutional networks(AGCN) is proposed. AGCN combines graph structure information and node attribute information to improve network representation learning performance, and enforces the latent codes to match a prior distribution. Moreover, an end-to-end multi-task learning framework(MTL) based on AGCN is introduced. It can perform link prediction and node classification simultaneously. The experiment shows that MTL achieves a good performance.

Key words： Network Representation Link Prediction Node Classification Multi-task Learning

Received: 24 August 2019

ZTFLH:

TP 183

Fund:Supported by National Natural Science Foundation of China(No.61972135,61602159), Natural Science Foundation of Hei longjiang Province ( No. F201430 ), Fundamental Research Funds of Universities in Heilongjiang Province(No. HDJCCX-201608,KJCX201815,KJCX201816), Innovation Talents Project of Science and Technology Bureau of Harbin(No.2017RAQXJ094,2017RAQXJ131)

Corresponding Authors: LIU Yong, Ph.D., associate professor. His research interests include data mining and database.

About author:: CHEN Mengxue, master student. Her research interests include link prediction.

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Cite this article:

CHEN Mengxue,LIU Yong. Network Representation Learning Framework Based on Adversarial Graph Convolutional Networks[J]. , 2019, 32(11): 1042-1050.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201911009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I11/1042

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